---
title: DataRobot model in a PPS
description: How to deploy a DataRobot model in a Portable Prediction Server.

---

# Deploy a DataRobot model in a Portable Prediction Server {: #deploy-a-datarobot-model-in-a-portable-prediction-server }

DataRobot AutoML models can be deployed to a containerized DataRobot prediction environment called a Portable Prediction Server (PPS). To deploy an AutoML model to a PPS, you can build models with AutoML, deploy a chosen model to an external prediction environment, and then deploy the model package in a PPS with monitoring enabled. Once deployed, you can monitor this portable model alongside models deployed in DataRobot prediction environments.

To create and deploy an AutoML model in a PPS, follow the workflow outlined below:

``` mermaid
graph TB
  A[Register a model] --> B{Create an external prediction environment?}
  B --> |No|C[Deploy the model to an external prediction environment]
  B --> |Yes|D[Add an external prediction environment]
  D --> C 
  C --> E[Deploy the model package to a PPS]
```

## Register a model {: #register-a-model }

DataRobot AutoML automatically generates models and displays them on the Leaderboard. The [model recommended for deployment](model-rec-process) appears at the top of the page. You can register this (or any other) model to the Model Registry directly from the Leaderboard.

[Register a model <span style="vertical-align: sub">:material-arrow-right-circle:{.lg }</span>](dr-model-reg){ .md-button }

## Deploy the model externally to a PPS {: #deploy-the-model-to-a-pps }

The Portable Prediction Server (PPS) is a solution for deploying a DataRobot model to an external prediction environment. You can download the PPS from the developer tools and use it to deploy a model package from the Model Registry. Once running, the PPS installation serves predictions via the DataRobot API.

!!! note
      Depending on the [MLOps configuration](pricing) for your organization, you may be able to [download the PPS model package from the Leaderboard](portable-pps#leaderboard-download) for external deployment. However, without associating the model package with an external prediction environment, you won't be able to monitor the model's predictions.

### Optional: Add an external prediction environment {: #add-an-external-prediction-environment }

To create an MLOps model deployment compatible with the PPS, you must add the model package to an external prediction environment. Create an external prediction environment if you don't already have one in DataRobot.

[Add an external prediction environment <span style="vertical-align: sub">:material-arrow-right-circle:{.lg }</span>](pred-env){ .md-button }

### Deploy the model package to an external prediction environment {: #deploy-the-model-package-to-an-external-prediction-environment }

To create an MLOps deployment with an external prediction environment, deploy a model package to an external prediction environment. 

[Deploy a model to an external prediction environment <span style="vertical-align: sub">:material-arrow-right-circle:{.lg }</span>](deploy-model#deploy-from-the-model-registry){ .md-button }

### Deploy the model package to a PPS {: #deploy-the-model-package-to-a-PPS }

The model's PPS model package (`.mlpkg`) file and the command-line snippet used to initiate the PPS with monitoring are provided for any model tagged as having an [external prediction environment](pred-env) in the deployment inventory. You can download the model's PPS model package and use the provided docker commands to deploy the model with monitoring enabled.

[Deploy a model to a PPS <span style="vertical-align: sub">:material-arrow-right-circle:{.lg }</span>](portable-pps){ .md-button }